In modern enterprise systems, AI adoption is outpacing security methods. Traditional SSDLC, focused on static code analysis, proves ineffective against the non-deterministic nature of AI models. The primary challenge for technical leaders is the gap between development speed and controlling unpredictable model behavior, requiring a shift from source code security to comprehensive data and context management.
Why traditional SSDLC fails to detect AI risks
Classic SAST tools cannot detect logical manipulations like Prompt Injection (LLM01:2025). According to OWASP, this is a primary risk for GenAI applications. When a system interacts with an LLM, the security perimeter blurs: attacks may target the model's context window rather than the code. LLM02:2025 (Sensitive Information Disclosure) is also critical, where data leaks occur through model responses or integrated tools.
Threat modeling for AI: from MITRE ATLAS to real-world threats
During the design phase, it is essential to structure threats using MITRE ATLAS. This allows for the classification of attacker tactics and the execution of AI Red Teaming. Instead of static checks, we model scenarios where the model might "hallucinate" or leak sensitive data, enabling the implementation of appropriate detectors at the architectural level.
Adapting SSDLC: a security checklist for every stage
According to NIST AI RMF 1.0, AI risk management should be based on four functions: Govern, Map, Measure, and Manage. This transforms SSDLC into a continuous process:
- Design: Threat modeling using MITRE ATLAS.
- Development: Data sanitization before input into the context window.
- Deployment: Implementing architectural guardrails to filter requests/responses.
- Operations: End-to-end audit trail of all model interactions.
- Monitoring: Implementing SLI/SLO for model accuracy and latency (based on Google SRE methodology).
Architectural foundation: how UnityBase ensures control over AI services
To minimize risks in complex systems, the UnityBase platform provides a technical foundation that allows for AI integration within a controlled perimeter. By utilizing domain metadata, developers can configure RBAC and RLS to strictly limit the data transmitted to the model. Centralized audit trails in the system allow for tracking every interaction, ensuring accountability. This is critical for solutions built on the UnityBase platform, where data security remains a priority during integration with external AI services.
Compliance and ISO/IEC 42001:2023 as a maturity indicator
Certification under the ISO/IEC 42001:2023 standard confirms the existence of structured risk management rather than providing automatic protection. It demonstrates that an organization takes a responsible approach to the AI development lifecycle. For enterprises, this means combining organizational policies with technical mechanisms to form a resilient security system.
Checklist for AI-Security Layer in SSDLC
| Stage | Security criteria |
|---|---|
| Design | Threat modeling (MITRE ATLAS) |
| Development | Testing for prompt injection, data validation |
| Deployment | Implementation of architectural guardrails |
| Operations | End-to-end audit trail |
| Monitoring | Establishing SLI/SLO |
FAQ
How to protect a RAG system from prompt injection?
Use multi-level filtering: input sanitization and architectural guardrails that verify model output before sending it to the user.
Is ISO/IEC 42001 sufficient for AI security in the enterprise?
It is a management framework for risk assessment. It requires technical implementation, such as auditing, access control (RBAC), and SLI/SLO monitoring.
How to integrate AI models into the UnityBase security perimeter?
Use the platform's domain metadata and RBAC/RLS to restrict data access, and utilize the built-in audit trail to log every request to the AI service.
Data sources
- OWASP: Top 10 Risk & Mitigations for LLMs and Gen AI Apps 2025
- MITRE ATLAS
- NIST: Artificial Intelligence Risk Management Framework (AI RMF 1.0)
- Google Site Reliability Engineering (SRE) Book
- vertexaisearch.cloud.google.com: Secure Software Development Lifecycle (SSDLC) - Best Practices & Benefits - PureDome